Detecting climate change impacts on ocean primary productivity is hindered by the shortness of the record and the long timescale of memory within the ocean. As a result, time-series analysis of satellite ocean chlorophyll is still inconclusive as to the sign of change in some regions. In this talk, we will discuss a Bayesian hierarchical space-time model used to estimate long-term trends in satellite ocean chlorophyll. Utilizing spatial dependency in the available data improves the model fit, but reveals large uncertainties. We will discuss the use of coupled model simulations from the CMIP5 experiment to form priors to provide a “first guess” on observational trend estimates and to constrain uncertainties. The Bayesian hierarchical model used here provides a framework for integrating different sources of data for detecting trends and estimating their uncertainty in studies of global change.